1 Overview

1.1 Data inspection

1.2 Participants

A total of 1146 participants were recruited through a survey posted on Prolific. 184 were excluded as they did not complete the survey, and 98 were excluded as they are vegan/vegetarian, and 5 were excluded for indicating that their results should not be included in the analysis. 13 were excluded for failing to select the correct response in an attention check. The final sample (N = 846) ranged in age 18 to 79 (Mdnage = 34.50, Mage = 37.21, SD = 13.58). The participants were predominantly female (56.38%). The participants received £0.35 ($0.45) for successfully completing the task.

1.3 Randomization check

A preliminary randomization check was conducted. The check revealed no systematic differences between the three conditions in gender, age, political position, and nationality (all p’s > .05).

Table 1.1: Randomisation check
Item Dynamic Static No norm Significance test
Age (years) 37.34 \(\pm\) 14.22 37.90 \(\pm\) 12.97 36.40 \(\pm\) 13.55 \(F(2, 843) = 0.89\), \(\mathit{MSE} = 184.49\), \(p = .409\)
Gender (%) Male (39.49%) Female (60.14%) Other (0.36%) Male (41.9%) Female (58.1%) Male (48.25%) Female (51.05%) Other (0.7%) \(\chi^2(4, n = 846) = 6.92\), \(p = .140\)
Political position 3.47 \(\pm\) 1.22 3.54 \(\pm\) 1.26 3.45 \(\pm\) 1.26 \(F(2, 843) = 0.39\), \(\mathit{MSE} = 1.55\), \(p = .679\)
Nationality (%) 1 (84.06%) 2 (3.62%) 3 (9.78%) 4 (0.72%) NA (1.81%) 1 (80.99%) 2 (5.28%) 3 (8.45%) 4 (1.41%) NA (3.87%) 1 (80.77%) 2 (5.94%) 3 (8.74%) 4 (1.75%) NA (2.8%) \(\chi^2(6, n = 846) = 3.23\), \(p = .779\)

1.4 Correlations

Table 1.2: Means, Standard Deviations, Reliabilities, and Inter-Correlations Among Study Measures
Alpha M SD 1 2 3 4 5 6
Interest
3.58 1.82
Attitude 0.90 4.64 1.29 .80**
Intention 0.98 4.22 1.80 .83** .82**
Expectation 0.99 3.93 1.75 .80** .80** .92**
Intent/expectation composite
4.08 1.74 .83** .83** .98** .98**
Perception of change
5.14 0.90 .27** .26** .27** .25** .27**
Preconformity
4.18 1.20 .44** .40** .39** .37** .39** .37**

2 Confirmatory analyses

2.1 Does making dynamic norms about reduced meat consumption in the UK salient lead to higher interest in reducing meat consumption (compared to static norm salience)?

Sparkman and Walton (2017) found effects of dynamic norms on interest in reducing meat consumption ranging from Mdiff = 0.60 – 0.78. Thus, the rough mean difference between dynamic and static norms expected in the sample is 0.69 on a 7 point Likert scale. Thus, I modeled H1 as a half-normal with an SD of 0.69. The plausible maximum effect was set at 1.38.

World cloud of participants response to text

Figure 2.1: World cloud of participants response to text

Table 2.1:
Most frequent words in text
word freq
health 216.00
environment 193.00
animals 144.00
climate 67.00
awareness 64.00
concerns 64.00
impact 56.00
media 50.00
vegan 43.00
better 35.00

The mean interest for participants in the dynamic norm condition was M = 3.64 (SD = 1.83), and the mean interest in the static norm condition was M = 3.68 (SD = 1.84). The mean interest in the no norm condition was M = 3.41 (SD = 1.77).

There was no difference in interest in reducing meat consumption between the dynamic norm (M = 3.64, SD = 1.83) and static norm (M = 3.68, SD = 1.84) conditions, \(\Delta M = -0.03\), 95% CI \([-0.34\), \(0.27]\), \(t(843) = -0.23\), \(p = .821\), d = -0.02, \(B_{\text{HN}(0, 0.69)}\) = 0.18, RR[0.65, 2].

Participants in the no-norm control condition showed the least interest in reducing meat consumption (M = 3.41, SD = 1.77) and did not differ from those in the dynamic-norm condition \(\Delta M = 0.23\), 95% CI \([-0.07\), \(0.53]\), \(t(843) = 1.52\), \(p = .130\), d = 0.13, or the static-norm condition \(\Delta M = 0.27\), 95% CI \([-0.03\), \(0.57]\), \(t(843) = 1.76\), \(p = .080\), d = 0.15. There was also no difference between the dynamic-norm condition and a combination of the control and static-norm conditions \(\Delta M = 0.10\), 95% CI \([-0.16\), \(0.36]\), \(t(843) = 0.74\), \(p = .458\).

Table 2.2:
Meat consumption by condition contrasts
contrast \(\Delta M\) 95% CI \(t(843)\) \(p\)
DYST Dynamic, static -0.03 \([-0.34\), \(0.27]\) -0.23 .821
DYNO Dynamic, control 0.23 \([-0.07\), \(0.53]\) 1.52 .130
STNO Static, control 0.27 \([-0.03\), \(0.57]\) 1.76 .080
DYCONT Dynamic, both 0.10 \([-0.16\), \(0.36]\) 0.74 .458
EXPNO Norms, control -0.25 \([-0.51\), \(0.01]\) -1.89 .059

2.2 Will participants in the dynamic norm condition be more likely (than those in the static norm control) to predict a future decrease in meat consumption in the UK?

I modeled H2 using a half-normal distribution with a mean of 0 and SD of Mdiff = 0.40. The plausible maximum effect was set at twice the predicted effect of Mdiff = 0.80. A Bayes factor was calculated for each test.

Table 2.3: Expectations of future meat consumption
Future Norm
Preconformity
Combined
Condition \(n\) \(M\) \(SD\) \(M\) \(SD\) \(M\) \(SD\)
Dynamic 276 5.26 0.93 4.35 1.18 4.81 0.88
Static 284 5.20 0.85 4.24 1.19 4.72 0.85
No norm 286 4.97 0.89 3.95 1.18 4.46 0.84

2.2.0.1 Measure of perception of change: “In the next 5 years, I expect meat consumption in the UK to…”

There was no evidence one way or another for an effect of dynamic norm condition on expectations about future meat consumption, \(\Delta M = 0.06\), 95% CI \([-0.08\), \(0.21]\), \(t(843) = 0.85\), \(p = .397\), d = 0.07, \(B_{\text{HN}(0, 0.40)}\) = 0.42, RR[0.05, 0.8]

Table 2.4: Perception change contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.06 \([-0.08\), \(0.21]\) 0.85 .397
DYNO Dynamic, control 0.30 \([0.15\), \(0.44]\) 3.94 < .001
STNO Static, control 0.23 \([0.09\), \(0.38]\) 3.11 .002
DYCONT Dynamic, both 0.18 \([0.05\), \(0.31]\) 2.75 .006
EXPNO Norms, control -0.26 \([-0.39\), \(-0.14]\) -4.08 < .001

2.2.0.2 Measure of preconformity: “In the foreseeable future, to what extent do you think that many people will make an effort to eat less meat?”

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.35, SD = 1.18) and static norm (M = 4.24, SD = 1.19) conditions, \(\Delta M = 0.11\), 95% CI \([-0.09\), \(0.31]\), \(t(843) = 1.08\), \(p = .279\), d = 0.09, \(B_{\text{HN}(0, 0.40)}\) = 0.72, RR[0.05, 1.5].

Table 2.5: Preconformity contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.11 \([-0.09\), \(0.31]\) 1.08 .279
DYNO Dynamic, control 0.40 \([0.20\), \(0.60]\) 4.00 < .001
STNO Static, control 0.29 \([0.10\), \(0.49]\) 2.94 .003
DYCONT Dynamic, both 0.25 \([0.08\), \(0.43]\) 2.93 .004
EXPNO Norms, control -0.35 \([-0.52\), \(-0.18]\) -4.02 < .001

2.2.0.3 Combined

There was no evidence one way or the other for there being a difference in anticipation that many people would make an effort to reduce their meat consumption in the future between the dynamic norm (M = 4.81, SD = 0.88) and static norm (M = 4.72, SD = 0.85) conditions, \(\Delta M = 0.09\), 95% CI \([-0.06\), \(0.23]\), \(t(843) = 1.19\), \(p = .235\), d = 0.10, \(B_{\text{HN}(0, 0.40)}\) = 0.62, RR[0.05, 1.25].

Table 2.6: Combined contrasts
contrast estimate ci statistic p.value
DYST Dynamic, static 0.09 \([-0.06\), \(0.23]\) 1.19 .235
DYNO Dynamic, control 0.35 \([0.21\), \(0.49]\) 4.81 < .001
STNO Static, control 0.26 \([0.12\), \(0.40]\) 3.65 < .001
DYCONT Dynamic, both 0.22 \([0.09\), \(0.34]\) 3.45 .001
EXPNO Norms, control -0.31 \([-0.43\), \(-0.18]\) -4.89 < .001

3 Secondary analyses

3.1 Will there be a difference in perceptions of current static norm across the dynamic and static norm conditions?

The SESOI for percentage difference is ± 5%. The SESOI for mean difference on the Likert scale is ± 0.5.

TOST results: t-value lower bound: 129.959 p-value lower bound: 0e+00 t-value upper bound: \(-23.608\) p-value upper bound: \(2.99\times10^{-86}\) degrees of freedom : 557.66

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: 3.355 upper bound 90% CI: 3.57

NHST confidence interval: lower bound 95% CI: 3.335 upper bound 95% CI: 3.591

Equivalence Test Result: The equivalence test was significant, t(557.66) = \(-23.608\), p = \(2.99\times10^{-86}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was significant, t(557.66) = 53.175, p = \(1.51\times10^{-220}\), given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically different from zero and statistically equivalent to zero.

3.2 Will there be a difference in how meat consumption is construed across the dynamic and static norm conditions?

The SESOI for difference in number of meals is ± 2 meals.

TOST results: t-value lower bound: 15.086 p-value lower bound: \(1.12\times10^{-43}\) t-value upper bound: \(-13.542\) p-value upper bound: \(1.25\times10^{-36}\) degrees of freedom : 557.55

Equivalence bounds (raw scores): low eqbound: \(-5\) high eqbound: 5

TOST confidence interval: lower bound 90% CI: \(-0.306\) upper bound 90% CI: 0.845

NHST confidence interval: lower bound 95% CI: \(-0.417\) upper bound 95% CI: 0.956

Equivalence Test Result: The equivalence test was significant, t(557.55) = \(-13.542\), p = \(1.25\times10^{-36}\), given equivalence bounds of \(-5\) and 5 (on a raw scale) and an alpha of 0.05. Null Hypothesis Test Result: The null hypothesis test was non-significant, t(557.55) = 0.772, p = .441, given an alpha of 0.05. Based on the equivalence test and the null-hypothesis test combined, we can conclude that the observed effect is statistically not different from zero and statistically equivalent to zero.

4 Exploratory analyses

4.1 Does dynamic norm (versus static norm) information lead to more positive attitudes, intentions, and expectations to reduce meat consumption?

4.1.1 Trace plots

Traceplots of regression parameters

Figure 4.1: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.2: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.3: Traceplots of regression parameters

4.1.2 Posterior plots

Posterior uncertainty intervals

Figure 4.4: Posterior uncertainty intervals

Posterior density plot

Figure 4.5: Posterior density plot

4.1.3 Summary table

Table 4.1: Posterior results for simple model (H3)
Model 1: Uninformative priors\(^a\)
Model 2: Weakly informative priors\(^b\)
Model 3: Moderately informative priors\(^c\)
Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest 0.040 (0.156) -0.265, 0.349 819.155 1.003 normal(0,10) 0.089 (0.154) -0.212, 0.388 771.276 1.005 122.5 normal(0.5,0.75) 0.196 (0.132) -0.059, 0.459 889.294 1.004 390 normal(0.5, 0.35)
Attitude -0.049 (0.111) -0.267, 0.165 771.073 1.004 normal(0,10) -0.012 (0.112) -0.234, 0.207 730.007 1.005 -75.51 normal(0.5,0.75) 0.064 (0.093) -0.117, 0.244 928.977 1.003 -230.61 normal(0.5, 0.35)
Intention/Expectation -0.036 (0.144) -0.314, 0.251 808.662 1.005 normal(0,10) 0.011 (0.145) -0.271, 0.291 725.346 1.006 -130.56 normal(0.5,0.75) 0.113 (0.122) -0.12, 0.353 931.226 1.004 -413.89 normal(0.5, 0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .499 b ppp = .493 c ppp = .452

4.2 Does age interact with norm condition to influence dependent variables?

4.2.1 Trace plots

Traceplots of regression parameters

Figure 4.6: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.7: Traceplots of regression parameters

Traceplots of regression parameters

Figure 4.8: Traceplots of regression parameters

4.2.2 Posterior plots

Posterior uncertainty intervals

Figure 4.9: Posterior uncertainty intervals

4.2.3 Summary table

Table 4.2: Posterior results for multi-sample analysis by age (H4)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Young adults
Interest 0.233 (0.330) -0.407, 0.835 1026.013 1 normal(0,10) 0.369 (0.279) -0.146, 0.927 1167.716 1.003 58.37 normal(0.5,0.75) 0.512 (0.214) 0.076, 0.928 1199.913 1 119.74 normal(0.5,0.35)
Attitude 0.090 (0.240) -0.39, 0.548 977.768 1.001 normal(0,10) 0.190 (0.207) -0.203, 0.608 1009.094 1.004 111.11 normal(0.5,0.75) 0.320 (0.165) 0.015, 0.662 1227.428 1 255.56 normal(0.5,0.35)
Intention/Expectation -0.099 (0.303) -0.7, 0.464 946.394 1.001 normal(0,10) 0.038 (0.262) -0.448, 0.565 1067.330 1.003 -138.38 normal(0.5,0.75) 0.214 (0.203) -0.18, 0.624 1245.482 1 -316.16 normal(0.5,0.35)
Middle-aged adults
Interest 0.071 (0.230) -0.381, 0.512 950.455 1.004 normal(0,10) 0.153 (0.205) -0.26, 0.557 1241.663 0.999 115.49 normal(0.5,0.75) 0.322 (0.163) 0, 0.639 1274.756 1 353.52 normal(0.5,0.35)
Attitude -0.057 (0.169) -0.381, 0.277 1025.134 1.004 normal(0,10) 0.001 (0.151) -0.289, 0.298 1307.024 0.999 -101.75 normal(0.5,0.75) 0.129 (0.127) -0.122, 0.384 1272.112 1.001 -326.32 normal(0.5,0.35)
Intention/Expectation 0.026 (0.224) -0.404, 0.466 958.833 1.004 normal(0,10) 0.103 (0.199) -0.273, 0.498 1223.354 1 296.15 normal(0.5,0.75) 0.271 (0.161) -0.05, 0.597 1294.632 1 942.31 normal(0.5,0.35)
Old adults
Interest -0.154 (0.274) -0.684, 0.391 1307.916 1.002 normal(0,10) -0.007 (0.255) -0.488, 0.52 1039.340 1.001 -95.45 normal(0.5,0.75) 0.240 (0.196) -0.135, 0.632 1124.467 1.003 -255.84 normal(0.5,0.35)
Attitude -0.121 (0.181) -0.488, 0.232 1340.006 1.001 normal(0,10) -0.023 (0.175) -0.362, 0.32 1081.721 1.002 -80.99 normal(0.5,0.75) 0.142 (0.137) -0.133, 0.413 1221.826 1.002 -217.36 normal(0.5,0.35)
Intention/Expectation -0.064 (0.253) -0.562, 0.437 1331.828 1.002 normal(0,10) 0.065 (0.239) -0.395, 0.519 1090.644 1 -201.56 normal(0.5,0.75) 0.283 (0.183) -0.069, 0.649 1138.656 1.002 -542.19 normal(0.5,0.35)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .486 b ppp = .485 c ppp = .388
Table 4.3: Posterior results for moderation model using age as continuous variable (H4)
Estimate Post.SD pi.lower pi.upper Rhat neff Prior
Interest
conditionbi 0.053 0.157 -0.25 0.367 1.000 842.822 normal(0,10)
age_cent -0.017 0.008 -0.033 0 1.001 941.738 normal(0,10)
condition_age 0.012 0.012 -0.01 0.035 1.000 956.013 normal(0,10)
Attitudes
conditionbi -0.042 0.113 -0.266 0.182 1.000 828.461 normal(0,10)
age_cent -0.008 0.006 -0.02 0.004 1.001 939.182 normal(0,10)
condition_age 0.006 0.008 -0.01 0.022 1.001 916.168 normal(0,10)
Intentions/Expectations
conditionbi -0.038 0.149 -0.332 0.264 1.001 831.300 normal(0,10)
age_cent 0.001 0.008 -0.014 0.017 1.001 881.743 normal(0,10)
condition_age -0.001 0.011 -0.023 0.02 1.000 897.894 normal(0,10)

4.3 Do demographic factors such as age, gender, and political position predict the primary dependent variables?

4.3.1 Trace plots

Traceplots for estimated regression parameters

Figure 4.10: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.11: Traceplots for estimated regression parameters

Traceplots for estimated regression parameters

Figure 4.12: Traceplots for estimated regression parameters

4.3.2 Posterior plots

Posterior uncertainty intervals

Figure 4.13: Posterior uncertainty intervals

Posterior density plot

Figure 4.14: Posterior density plot

4.3.3 Summary table

Table 4.4: Posterior results for full model (H5)
Model 1: Uninformative priors\(^a\)
Model 2: Informative priors\(^b\)
Model 3: Informative priors\(^c\)
Parameter Mean (SD) 95% PPI neff PSRF Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior Mean (SD) 95% PPI neff PSRF Bias (%) Prior
Interest
Condition 0.063 (0.150) -0.232, 0.364 1639.072 1 normal(0,10) 0.105 (0.147) -0.194, 0.381 1535.778 0.999 66.67 normal(0.5,0.75) 0.216 (0.129) -0.048, 0.468 1416.422 1 242.86 normal(0.5,0.35)
Age -0.002 (0.006) -0.013, 0.009 2376.922 1 normal(0,10) -0.003 (0.006) -0.014, 0.009 2791.596 1 50 normal(0,10) -0.003 (0.006) -0.013, 0.008 2269.230 1 50 normal(0,10)
Gender 0.527 (0.154) 0.232, 0.831 1447.178 1.002 normal(0,10) 0.532 (0.157) 0.225, 0.841 1412.359 1.001 0.95 normal(0,10) 0.533 (0.150) 0.241, 0.817 1537.473 1.002 1.14 normal(0,10)
Politics -0.311 (0.060) -0.428, -0.196 1488.474 1.001 normal(0,10) -0.309 (0.062) -0.43, -0.186 1213.321 1 -0.64 normal(0,10) -0.309 (0.062) -0.432, -0.186 1365.964 1.001 -0.64 normal(0,10)
Attitudes
Condition -0.032 (0.107) -0.236, 0.183 1607.415 1.001 normal(0,10) -0.003 (0.106) -0.219, 0.199 1565.899 1 -90.62 normal(0.5,0.75) 0.075 (0.095) -0.111, 0.265 1451.491 1.001 -334.38 normal(0.5,0.35)
Age 0.001 (0.004) -0.007, 0.009 2319.560 1 normal(0,10) 0.001 (0.004) -0.007, 0.009 2684.022 1 0 normal(0,10) 0.001 (0.004) -0.007, 0.009 2628.132 1 0 normal(0,10)
Gender 0.298 (0.111) 0.089, 0.516 1303.598 1.003 normal(0,10) 0.303 (0.111) 0.09, 0.518 1316.893 1.001 1.68 normal(0,10) 0.304 (0.110) 0.091, 0.522 1500.929 1.002 2.01 normal(0,10)
Politics -0.244 (0.044) -0.331, -0.16 1436.536 1.002 normal(0,10) -0.244 (0.045) -0.331, -0.156 1290.656 1 0 normal(0,10) -0.242 (0.044) -0.328, -0.153 1268.313 1.001 -0.82 normal(0,10)
Intention/Expectations
Condition -0.019 (0.144) -0.308, 0.263 1598.190 1 normal(0,10) 0.021 (0.138) -0.259, 0.287 1507.211 1 -210.53 normal(0.5,0.75) 0.128 (0.122) -0.113, 0.373 1389.121 1.001 -773.68 normal(0.5,0.35)
Age 0.008 (0.005) -0.003, 0.018 2326.488 1 normal(0,10) 0.008 (0.005) -0.003, 0.018 2690.547 0.999 0 normal(0,10) 0.008 (0.005) -0.002, 0.018 2473.919 0.999 0 normal(0,10)
Gender 0.592 (0.148) 0.308, 0.89 1346.050 1.003 normal(0,10) 0.598 (0.148) 0.304, 0.89 1366.947 1 1.01 normal(0,10) 0.599 (0.146) 0.308, 0.881 1581.186 1.002 1.18 normal(0,10)
Politics -0.275 (0.057) -0.385, -0.165 1389.771 1.002 normal(0,10) -0.274 (0.060) -0.389, -0.156 1237.262 1 -0.36 normal(0,10) -0.273 (0.059) -0.39, -0.155 1313.996 1.001 -0.73 normal(0,10)
Note. PPI = posterior probability interval; PSRF = potential scale reduction factor; neff = effective sample size
a ppp = .507 b ppp = .508 c ppp = .476

5 Unregistered analyses

5.1 Power test

Sparkman and Walton (2017) found standardized effects of dynamic norms on interest in reducing meat consumption ranging from \(d\) = 0.31 – 0.41. To detect the average effect of \(d\) = 0.36, we would need 122 participants in each condition.

 Two-sample t test power calculation 

          n = 122.0922
          d = 0.36
  sig.level = 0.05
      power = 0.8
alternative = two.sided

NOTE: n is number in each group

5.2 Moderation of demographic variables

Table 5.1:
Exploring the effect moderation of demographic variables on attitudes
Predictor \(b\) 95% CI \(t(553)\) \(p\)
Intercept 5.46 \([4.95\), \(5.97]\) 21.17 < .001
ConditionbiStatic -0.18 \([-0.89\), \(0.52]\) -0.51 .613
GenderbiFemale 0.35 \([0.04\), \(0.66]\) 2.21 .027
POLITICS -0.27 \([-0.40\), \(-0.15]\) -4.32 < .001
ConditionbiStatic \(\times\) GenderbiFemale -0.10 \([-0.53\), \(0.33]\) -0.47 .642
ConditionbiStatic \(\times\) POLITICS 0.06 \([-0.11\), \(0.23]\) 0.67 .505
Table 5.2:
Exploring the effect moderation of demographic variables on intention
Predictor \(b\) 95% CI \(t(553)\) \(p\)
Intercept 4.78 \([4.11\), \(5.45]\) 14.02 < .001
ConditionbiStatic -0.18 \([-1.12\), \(0.75]\) -0.39 .697
GenderbiFemale 0.78 \([0.37\), \(1.19]\) 3.77 < .001
POLITICS -0.32 \([-0.49\), \(-0.16]\) -3.85 < .001
ConditionbiStatic \(\times\) GenderbiFemale -0.41 \([-0.98\), \(0.16]\) -1.41 .159
ConditionbiStatic \(\times\) POLITICS 0.12 \([-0.11\), \(0.34]\) 1.00 .317
Table 5.3:
Exploring the effect moderation of demographic variables on interest
predictor \(b\) 95% CI \(t(553)\) \(p\)
Intercept 4.61 \([3.90\), \(5.31]\) 12.80 < .001
Condition -0.27 \([-1.25\), \(0.72]\) -0.53 .594
Gender 0.53 \([0.10\), \(0.96]\) 2.43 .015
Political position -0.37 \([-0.54\), \(-0.19]\) -4.14 < .001
Condition \(\times\) Gender -0.01 \([-0.61\), \(0.59]\) -0.03 .976
Condition \(\times\) Political position 0.09 \([-0.15\), \(0.33]\) 0.77 .443

5.3 Mediation

Table 5.4: Exploring mediating effect of attitude on intentions/expectations
Parameter \(M\) \(SD\) Lower PPI Upper PPI Rhat Prior
Direct effects
Interest ~ Condition 0.033 0.157 -0.272 0.35 1.004 normal(0,10)
Attitude ~ Condition -0.051 0.112 -0.272 0.161 0.999 normal(0,10)
Intent/Expectation ~ Condition -0.009 0.097 -0.196 0.192 1.004 normal(0,10)
Intent/Expectation ~ Attitude 0.625 0.048 0.531 0.721 1.000 normal(0,10)
Mediation
Indirect effects/nCondition > Attitude > Intention/Expectation -0.032 0.070 -0.169 0.106 NA
Total effect -0.040 0.120 -0.275 0.195 NA

6 Environment and data

6.1 Session information

## ─ Session info ───────────────────────────────────────────────────────────────
##  setting  value                       
##  version  R version 4.1.0 (2021-05-18)
##  os       macOS Big Sur 10.16         
##  system   x86_64, darwin17.0          
##  ui       X11                         
##  language (EN)                        
##  collate  en_GB.UTF-8                 
##  ctype    en_GB.UTF-8                 
##  tz       Europe/London               
##  date     2021-06-29                  
## 
## ─ Packages ───────────────────────────────────────────────────────────────────
##  package      * version    date       lib source                        
##  abind          1.4-5      2016-07-21 [1] CRAN (R 4.1.0)                
##  assertthat     0.2.1      2019-03-21 [1] CRAN (R 4.1.0)                
##  backports      1.2.1      2020-12-09 [1] CRAN (R 4.1.0)                
##  bayesplot      1.8.1      2021-06-14 [1] CRAN (R 4.1.0)                
##  bfrr         * 0.0.0.9000 2021-06-29 [1] Github (debruine/bfrr@9b80a99)
##  bitops         1.0-7      2021-04-24 [1] CRAN (R 4.1.0)                
##  bookdown       0.22       2021-04-22 [1] CRAN (R 4.1.0)                
##  boot           1.3-28     2021-05-03 [1] CRAN (R 4.1.0)                
##  broom          0.7.8      2021-06-24 [1] CRAN (R 4.1.0)                
##  bslib          0.2.5.1    2021-05-18 [1] CRAN (R 4.1.0)                
##  cachem         1.0.5      2021-05-15 [1] CRAN (R 4.1.0)                
##  callr          3.7.0      2021-04-20 [1] CRAN (R 4.1.0)                
##  car            3.0-11     2021-06-27 [1] CRAN (R 4.1.0)                
##  carData        3.0-4      2020-05-22 [1] CRAN (R 4.1.0)                
##  cellranger     1.1.0      2016-07-27 [1] CRAN (R 4.1.0)                
##  cli            2.5.0      2021-04-26 [1] CRAN (R 4.1.0)                
##  coda           0.19-4     2020-09-30 [1] CRAN (R 4.1.0)                
##  codebook     * 0.9.2      2020-06-06 [1] CRAN (R 4.1.0)                
##  colorspace     2.0-2      2021-06-24 [1] CRAN (R 4.1.0)                
##  crayon         1.4.1      2021-02-08 [1] CRAN (R 4.1.0)                
##  curl           4.3.2      2021-06-23 [1] CRAN (R 4.1.0)                
##  data.table     1.14.0     2021-02-21 [1] CRAN (R 4.1.0)                
##  DBI            1.1.1      2021-01-15 [1] CRAN (R 4.1.0)                
##  dbplyr         2.1.1      2021-04-06 [1] CRAN (R 4.1.0)                
##  desc           1.3.0      2021-03-05 [1] CRAN (R 4.1.0)                
##  devtools       2.4.2      2021-06-07 [1] CRAN (R 4.1.0)                
##  digest         0.6.27     2020-10-24 [1] CRAN (R 4.1.0)                
##  dplyr        * 1.0.7      2021-06-18 [1] CRAN (R 4.1.0)                
##  ellipsis       0.3.2      2021-04-29 [1] CRAN (R 4.1.0)                
##  emmeans      * 1.6.1      2021-06-01 [1] CRAN (R 4.1.0)                
##  estimability   1.3        2018-02-11 [1] CRAN (R 4.1.0)                
##  evaluate       0.14       2019-05-28 [1] CRAN (R 4.1.0)                
##  ez             4.4-0      2016-11-02 [1] CRAN (R 4.1.0)                
##  fansi          0.5.0      2021-05-25 [1] CRAN (R 4.1.0)                
##  farver         2.1.0      2021-02-28 [1] CRAN (R 4.1.0)                
##  fastmap        1.1.0      2021-01-25 [1] CRAN (R 4.1.0)                
##  forcats      * 0.5.1      2021-01-27 [1] CRAN (R 4.1.0)                
##  foreign        0.8-81     2020-12-22 [1] CRAN (R 4.1.0)                
##  fs             1.5.0      2020-07-31 [1] CRAN (R 4.1.0)                
##  generics       0.1.0      2020-10-31 [1] CRAN (R 4.1.0)                
##  ggplot2      * 3.3.5      2021-06-25 [1] CRAN (R 4.1.0)                
##  ggridges       0.5.3      2021-01-08 [1] CRAN (R 4.1.0)                
##  glue           1.4.2      2020-08-27 [1] CRAN (R 4.1.0)                
##  gridExtra      2.3        2017-09-09 [1] CRAN (R 4.1.0)                
##  gtable         0.3.0      2019-03-25 [1] CRAN (R 4.1.0)                
##  haven          2.4.1      2021-04-23 [1] CRAN (R 4.1.0)                
##  here         * 1.0.1      2020-12-13 [1] CRAN (R 4.1.0)                
##  highr          0.9        2021-04-16 [1] CRAN (R 4.1.0)                
##  hms            1.1.0      2021-05-17 [1] CRAN (R 4.1.0)                
##  htmltools      0.5.1.1    2021-01-22 [1] CRAN (R 4.1.0)                
##  httr           1.4.2      2020-07-20 [1] CRAN (R 4.1.0)                
##  insight        0.14.2     2021-06-22 [1] CRAN (R 4.1.0)                
##  jquerylib      0.1.4      2021-04-26 [1] CRAN (R 4.1.0)                
##  jsonlite       1.7.2      2020-12-09 [1] CRAN (R 4.1.0)                
##  kableExtra   * 1.3.4      2021-02-20 [1] CRAN (R 4.1.0)                
##  knitr        * 1.33       2021-04-24 [1] CRAN (R 4.1.0)                
##  labeling       0.4.2      2020-10-20 [1] CRAN (R 4.1.0)                
##  labelled       2.8.0      2021-03-08 [1] CRAN (R 4.1.0)                
##  lattice        0.20-44    2021-05-02 [1] CRAN (R 4.1.0)                
##  lifecycle      1.0.0      2021-02-15 [1] CRAN (R 4.1.0)                
##  lme4           1.1-27.1   2021-06-22 [1] CRAN (R 4.1.0)                
##  lubridate      1.7.10     2021-02-26 [1] CRAN (R 4.1.0)                
##  magrittr       2.0.1      2020-11-17 [1] CRAN (R 4.1.0)                
##  MASS         * 7.3-54     2021-05-03 [1] CRAN (R 4.1.0)                
##  Matrix         1.3-4      2021-06-01 [1] CRAN (R 4.1.0)                
##  MBESS          4.8.0      2020-08-05 [1] CRAN (R 4.1.0)                
##  memoise        2.0.0      2021-01-26 [1] CRAN (R 4.1.0)                
##  mgcv           1.8-36     2021-06-01 [1] CRAN (R 4.1.0)                
##  minqa          1.2.4      2014-10-09 [1] CRAN (R 4.1.0)                
##  mnormt         2.0.2      2020-09-01 [1] CRAN (R 4.1.0)                
##  modelr         0.1.8      2020-05-19 [1] CRAN (R 4.1.0)                
##  MOTE         * 1.0.2      2019-04-10 [1] CRAN (R 4.1.0)                
##  munsell        0.5.0      2018-06-12 [1] CRAN (R 4.1.0)                
##  mvtnorm        1.1-2      2021-06-07 [1] CRAN (R 4.1.0)                
##  nlme           3.1-152    2021-02-04 [1] CRAN (R 4.1.0)                
##  nloptr         1.2.2.2    2020-07-02 [1] CRAN (R 4.1.0)                
##  NLP          * 0.2-1      2020-10-14 [1] CRAN (R 4.1.0)                
##  openxlsx       4.2.4      2021-06-16 [1] CRAN (R 4.1.0)                
##  papaja       * 0.1.0.9997 2021-06-11 [1] Github (crsh/papaja@a231c36)  
##  pillar         1.6.1      2021-05-16 [1] CRAN (R 4.1.0)                
##  pkgbuild       1.2.0      2020-12-15 [1] CRAN (R 4.1.0)                
##  pkgconfig      2.0.3      2019-09-22 [1] CRAN (R 4.1.0)                
##  pkgload        1.2.1      2021-04-06 [1] CRAN (R 4.1.0)                
##  plyr           1.8.6      2020-03-03 [1] CRAN (R 4.1.0)                
##  prettyunits    1.1.1      2020-01-24 [1] CRAN (R 4.1.0)                
##  processx       3.5.2      2021-04-30 [1] CRAN (R 4.1.0)                
##  ps             1.6.0      2021-02-28 [1] CRAN (R 4.1.0)                
##  psy          * 1.1        2012-06-21 [1] CRAN (R 4.1.0)                
##  psych        * 2.1.6      2021-06-18 [1] CRAN (R 4.1.0)                
##  purrr        * 0.3.4      2020-04-17 [1] CRAN (R 4.1.0)                
##  qualtRics    * 3.1.4      2021-01-14 [1] CRAN (R 4.1.0)                
##  R6             2.5.0      2020-10-28 [1] CRAN (R 4.1.0)                
##  RColorBrewer * 1.1-2      2014-12-07 [1] CRAN (R 4.1.0)                
##  Rcpp         * 1.0.6      2021-01-15 [1] CRAN (R 4.1.0)                
##  RCurl        * 1.98-1.3   2021-03-16 [1] CRAN (R 4.1.0)                
##  readr        * 1.4.0      2020-10-05 [1] CRAN (R 4.1.0)                
##  readxl         1.3.1      2019-03-13 [1] CRAN (R 4.1.0)                
##  remotes        2.4.0      2021-06-02 [1] CRAN (R 4.1.0)                
##  reprex         2.0.0      2021-04-02 [1] CRAN (R 4.1.0)                
##  reshape        0.8.8      2018-10-23 [1] CRAN (R 4.1.0)                
##  reshape2       1.4.4      2020-04-09 [1] CRAN (R 4.1.0)                
##  rio            0.5.27     2021-06-21 [1] CRAN (R 4.1.0)                
##  rlang        * 0.4.11     2021-04-30 [1] CRAN (R 4.1.0)                
##  rmarkdown      2.9        2021-06-15 [1] CRAN (R 4.1.0)                
##  rprojroot      2.0.2      2020-11-15 [1] CRAN (R 4.1.0)                
##  rstudioapi     0.13       2020-11-12 [1] CRAN (R 4.1.0)                
##  rvest          1.0.0      2021-03-09 [1] CRAN (R 4.1.0)                
##  sass           0.4.0      2021-05-12 [1] CRAN (R 4.1.0)                
##  scales         1.1.1      2020-05-11 [1] CRAN (R 4.1.0)                
##  sessioninfo    1.1.1      2018-11-05 [1] CRAN (R 4.1.0)                
##  sjlabelled     1.1.8      2021-05-11 [1] CRAN (R 4.1.0)                
##  slam           0.1-48     2020-12-03 [1] CRAN (R 4.1.0)                
##  SnowballC    * 0.7.0      2020-04-01 [1] CRAN (R 4.1.0)                
##  stringi        1.6.2      2021-05-17 [1] CRAN (R 4.1.0)                
##  stringr      * 1.4.0      2019-02-10 [1] CRAN (R 4.1.0)                
##  svglite        2.0.0      2021-02-20 [1] CRAN (R 4.1.0)                
##  systemfonts    1.0.2      2021-05-11 [1] CRAN (R 4.1.0)                
##  testthat       3.0.3      2021-06-16 [1] CRAN (R 4.1.0)                
##  tibble       * 3.1.2      2021-05-16 [1] CRAN (R 4.1.0)                
##  tidyr        * 1.1.3      2021-03-03 [1] CRAN (R 4.1.0)                
##  tidyselect     1.1.1      2021-04-30 [1] CRAN (R 4.1.0)                
##  tidyverse    * 1.3.1      2021-04-15 [1] CRAN (R 4.1.0)                
##  tm           * 0.7-8      2020-11-18 [1] CRAN (R 4.1.0)                
##  tmvnsim        1.0-2      2016-12-15 [1] CRAN (R 4.1.0)                
##  TOSTER       * 0.3.4      2018-08-03 [1] CRAN (R 4.1.0)                
##  usethis        2.0.1      2021-02-10 [1] CRAN (R 4.1.0)                
##  utf8           1.2.1      2021-03-12 [1] CRAN (R 4.1.0)                
##  vctrs          0.3.8      2021-04-29 [1] CRAN (R 4.1.0)                
##  viridisLite    0.4.0      2021-04-13 [1] CRAN (R 4.1.0)                
##  webshot        0.5.2      2019-11-22 [1] CRAN (R 4.1.0)                
##  withr          2.4.2      2021-04-18 [1] CRAN (R 4.1.0)                
##  wordcloud    * 2.6        2018-08-24 [1] CRAN (R 4.1.0)                
##  xfun           0.24       2021-06-15 [1] CRAN (R 4.1.0)                
##  XML          * 3.99-0.6   2021-03-16 [1] CRAN (R 4.1.0)                
##  xml2           1.3.2      2020-04-23 [1] CRAN (R 4.1.0)                
##  xtable         1.8-4      2019-04-21 [1] CRAN (R 4.1.0)                
##  yaml           2.2.1      2020-02-01 [1] CRAN (R 4.1.0)                
##  zip            2.2.0      2021-05-31 [1] CRAN (R 4.1.0)                
## 
## [1] /Library/Frameworks/R.framework/Versions/4.1/Resources/library

6.2 Codebook

6.2.1 Metadata

6.2.1.1 Description

Dataset name: results

The dataset has N=846 rows and 34 columns. 0 rows have no missing values on any column.

Metadata for search engines
  • Date published: 2021-06-29
x
DYUK
STUK
UKNATION
RESIDENT
GENDER
condition
conditionbi
genderbi
age_cat
NATIONALITY
INTEREST
ATT1
ATT2
ATT3
INTENT1
INTENT2
INTENT3
EXPECT1
EXPECT2
EXPECT3
PERCEPTNUM
PERCEPTSCALE
CONSTRUAL_1
PERCEPTCHANGE
PRECONFORMITY
POLITICS
AGE
attitude_mean
intention_mean
expect_mean
expintent_avg
age_cent
PERCEPTCHANGE_r
comb_future

6.3 Codebook table

name data_type ordered value_labels n_missing complete_rate n_unique empty top_counts min median max mean sd whitespace hist DYUK STUK NATIONALITY label
<a href=“#DYUK”>DYUK</a> character NA NA 570 0.3262411 276 0 NA 7 NA 898 NA NA 0 NA Dynamic UK NA NA NA
<a href=“#STUK”>STUK</a> character NA NA 562 0.3356974 282 0 NA 3 NA 993 NA NA 0 NA NA Static UK NA NA
<a href=“#UKNATION”>UKNATION</a> factor FALSE
  1. 1,<br>2. 2,<br>3. 3,<br>4. 4
24 0.9716312 4 NA 1: 693, 3: 76, 2: 42, 4: 11 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#RESIDENT”>RESIDENT</a> factor FALSE
  1. 1,<br>2. 2
0 1.0000000 2 NA 1: 839, 2: 7 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#GENDER”>GENDER</a> factor FALSE
  1. Male,<br>2. Female,<br>3. Other
0 1.0000000 3 NA Fem: 477, Mal: 366, Oth: 3 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#condition”>condition</a> factor FALSE
  1. Dynamic,<br>2. Static,<br>3. No norm
0 1.0000000 3 NA No : 286, Sta: 284, Dyn: 276 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#conditionbi”>conditionbi</a> factor FALSE
  1. Dynamic,<br>2. Static
286 0.6619385 2 NA Sta: 284, Dyn: 276 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#genderbi”>genderbi</a> factor FALSE
  1. Male,<br>2. Female
3 0.9964539 2 NA Fem: 477, Mal: 366 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#age_cat”>age_cat</a> factor FALSE
  1. Young adults,<br>2. Middle-aged adults,<br>3. Old adults
0 1.0000000 3 NA Mid: 408, Old: 237, You: 201 NA NA NA NA NA NA NA NA NA NA NA
<a href=“#NATIONALITY”>NATIONALITY</a> numeric NA NA 0 1.0000000 NA NA NA 3 27.0 183 28.1264775 11.9352031 NA ▇▁▁▁▁ NA NA Nationality NA
<a href=“#INTEREST”>INTEREST</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.5780142 1.8171931 NA ▇▅▅▃▅ NA NA NA NA
<a href=“#ATT1”>ATT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.4739953 1.4647446 NA ▂▂▅▇▅ NA NA NA NA
<a href=“#ATT2”>ATT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.5851064 1.3911693 NA ▂▂▆▇▆ NA NA NA NA
<a href=“#ATT3”>ATT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.8758865 1.3734456 NA ▂▂▅▇▇ NA NA NA NA
<a href=“#INTENT1”>INTENT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1773050 1.8292186 NA ▆▃▃▇▇ NA NA NA NA
<a href=“#INTENT2”>INTENT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.1501182 1.8072618 NA ▆▃▅▇▆ NA NA NA NA
<a href=“#INTENT3”>INTENT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.3439716 1.8470845 NA ▆▂▃▇▇ NA NA NA NA
<a href=“#EXPECT1”>EXPECT1</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8959811 1.7864865 NA ▇▃▅▇▅ NA NA NA NA
<a href=“#EXPECT2”>EXPECT2</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.8640662 1.7840077 NA ▇▃▅▇▅ NA NA NA NA
<a href=“#EXPECT3”>EXPECT3</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.0378251 1.7564049 NA ▆▅▅▇▆ NA NA NA NA
<a href=“#PERCEPTNUM”>PERCEPTNUM</a> numeric NA NA 0 1.0000000 NA NA NA 3 30.0 85 28.7482270 11.1468611 NA ▂▇▂▁▁ NA NA NA NA
<a href=“#PERCEPTSCALE”>PERCEPTSCALE</a> numeric NA NA 0 1.0000000 NA NA NA 1 2.0 5 2.5685579 0.7485873 NA ▁▇▅▂▁ NA NA NA NA
<a href=“#CONSTRUAL_1”>CONSTRUAL_1</a> numeric NA NA 0 1.0000000 NA NA NA 1 10.0 21 11.0955083 4.1522252 NA ▂▆▇▇▂ NA NA NA NA
<a href=“#PERCEPTCHANGE”>PERCEPTCHANGE</a> numeric NA NA 0 1.0000000 NA NA NA 1 3.0 7 2.8605201 0.8980857 NA ▅▇▂▁▁ NA NA NA NA
<a href=“#PRECONFORMITY”>PRECONFORMITY</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 4.1796690 1.1959945 NA ▂▆▇▇▃ NA NA NA NA
<a href=“#POLITICS”>POLITICS</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.4905437 1.2448352 NA ▆▆▇▃▁ NA NA NA NA
<a href=“#AGE”>AGE</a> numeric NA NA 0 1.0000000 NA NA NA 18 34.5 79 37.2080378 13.5810251 NA ▇▆▅▂▁ NA NA NA NA
<a href=“#attitude_mean”>attitude_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 4.6449961 1.2912197 NA ▁▂▅▇▃ NA NA NA NA
<a href=“#intention_mean”>intention_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.7 7 4.2237983 1.7965065 NA ▅▃▃▇▆ NA NA NA NA
<a href=“#expect_mean”>expect_mean</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.0 7 3.9326241 1.7506056 NA ▆▅▅▇▅ NA NA NA NA
<a href=“#expintent_avg”>expintent_avg</a> numeric NA NA 0 1.0000000 NA NA NA 1 4.5 7 4.0782112 1.7381013 NA ▆▃▅▇▅ NA NA NA NA
<a href=“#age_cent”>age_cent</a> numeric NA NA 0 1.0000000 NA NA NA -19 -2.5 42 0.1955638 13.5810251 NA ▇▆▅▂▁ NA NA NA NA
<a href=“#PERCEPTCHANGE_r”>PERCEPTCHANGE_r</a> numeric NA NA 0 1.0000000 NA NA NA 1 5.0 7 5.1394799 0.8980857 NA ▁▁▂▇▅ NA NA NA NA
<a href=“#comb_future”>comb_future</a> numeric NA NA 0 1.0000000 NA NA NA 2 4.5 7 4.6595745 0.8692358 NA ▁▆▇▅▁ NA NA NA NA
JSON-LD metadata

The following JSON-LD can be found by search engines, if you share this codebook publicly on the web.

{
  "name": "results",
  "datePublished": "2021-06-29",
  "description": "The dataset has N=846 rows and 34 columns.\n0 rows have no missing values on any column.\n\n\n## Table of variables\nThis table contains variable names, labels, and number of missing values.\nSee the complete codebook for more.\n\n|name            |label | n_missing|\n|:---------------|:-----|---------:|\n|DYUK            |NA    |       570|\n|STUK            |NA    |       562|\n|UKNATION        |NA    |        24|\n|RESIDENT        |NA    |         0|\n|GENDER          |NA    |         0|\n|condition       |NA    |         0|\n|conditionbi     |NA    |       286|\n|genderbi        |NA    |         3|\n|age_cat         |NA    |         0|\n|NATIONALITY     |NA    |         0|\n|INTEREST        |NA    |         0|\n|ATT1            |NA    |         0|\n|ATT2            |NA    |         0|\n|ATT3            |NA    |         0|\n|INTENT1         |NA    |         0|\n|INTENT2         |NA    |         0|\n|INTENT3         |NA    |         0|\n|EXPECT1         |NA    |         0|\n|EXPECT2         |NA    |         0|\n|EXPECT3         |NA    |         0|\n|PERCEPTNUM      |NA    |         0|\n|PERCEPTSCALE    |NA    |         0|\n|CONSTRUAL_1     |NA    |         0|\n|PERCEPTCHANGE   |NA    |         0|\n|PRECONFORMITY   |NA    |         0|\n|POLITICS        |NA    |         0|\n|AGE             |NA    |         0|\n|attitude_mean   |NA    |         0|\n|intention_mean  |NA    |         0|\n|expect_mean     |NA    |         0|\n|expintent_avg   |NA    |         0|\n|age_cent        |NA    |         0|\n|PERCEPTCHANGE_r |NA    |         0|\n|comb_future     |NA    |         0|\n\n### Note\nThis dataset was automatically described using the [codebook R package](https://rubenarslan.github.io/codebook/) (version 0.9.2).",
  "keywords": ["DYUK", "STUK", "UKNATION", "RESIDENT", "GENDER", "condition", "conditionbi", "genderbi", "age_cat", "NATIONALITY", "INTEREST", "ATT1", "ATT2", "ATT3", "INTENT1", "INTENT2", "INTENT3", "EXPECT1", "EXPECT2", "EXPECT3", "PERCEPTNUM", "PERCEPTSCALE", "CONSTRUAL_1", "PERCEPTCHANGE", "PRECONFORMITY", "POLITICS", "AGE", "attitude_mean", "intention_mean", "expect_mean", "expintent_avg", "age_cent", "PERCEPTCHANGE_r", "comb_future"],
  "@context": "http://schema.org/",
  "@type": "Dataset",
  "variableMeasured": [
    {
      "name": "DYUK",
      "description": "Dynamic UK",
      "@type": "propertyValue"
    },
    {
      "name": "STUK",
      "description": "Static UK",
      "@type": "propertyValue"
    },
    {
      "name": "UKNATION",
      "value": "1. 1,\n2. 2,\n3. 3,\n4. 4",
      "@type": "propertyValue"
    },
    {
      "name": "RESIDENT",
      "value": "1. 1,\n2. 2",
      "@type": "propertyValue"
    },
    {
      "name": "GENDER",
      "value": "1. Male,\n2. Female,\n3. Other",
      "@type": "propertyValue"
    },
    {
      "name": "condition",
      "value": "1. Dynamic,\n2. Static,\n3. No norm",
      "@type": "propertyValue"
    },
    {
      "name": "conditionbi",
      "value": "1. Dynamic,\n2. Static",
      "@type": "propertyValue"
    },
    {
      "name": "genderbi",
      "value": "1. Male,\n2. Female",
      "@type": "propertyValue"
    },
    {
      "name": "age_cat",
      "value": "1. Young adults,\n2. Middle-aged adults,\n3. Old adults",
      "@type": "propertyValue"
    },
    {
      "name": "NATIONALITY",
      "description": "Nationality",
      "@type": "propertyValue"
    },
    {
      "name": "INTEREST",
      "@type": "propertyValue"
    },
    {
      "name": "ATT1",
      "@type": "propertyValue"
    },
    {
      "name": "ATT2",
      "@type": "propertyValue"
    },
    {
      "name": "ATT3",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT1",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT2",
      "@type": "propertyValue"
    },
    {
      "name": "INTENT3",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT1",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT2",
      "@type": "propertyValue"
    },
    {
      "name": "EXPECT3",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTNUM",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTSCALE",
      "@type": "propertyValue"
    },
    {
      "name": "CONSTRUAL_1",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE",
      "@type": "propertyValue"
    },
    {
      "name": "PRECONFORMITY",
      "@type": "propertyValue"
    },
    {
      "name": "POLITICS",
      "@type": "propertyValue"
    },
    {
      "name": "AGE",
      "@type": "propertyValue"
    },
    {
      "name": "attitude_mean",
      "@type": "propertyValue"
    },
    {
      "name": "intention_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expect_mean",
      "@type": "propertyValue"
    },
    {
      "name": "expintent_avg",
      "@type": "propertyValue"
    },
    {
      "name": "age_cent",
      "@type": "propertyValue"
    },
    {
      "name": "PERCEPTCHANGE_r",
      "@type": "propertyValue"
    },
    {
      "name": "comb_future",
      "@type": "propertyValue"
    }
  ]
}`